ABSTRACT: The needs for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. In this work we establish proof-of-concept that whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,873 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being non-silent (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at www.sysbio.se/cenpk. Keywords: Two strains and two different carbon sources Two conditions (glucose and galactose) with two biological replicates for S. cerevisiae strains S288c and CEN.PK113-7D

Similar Datasets

Project description:The needs for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. In this work we establish proof-of-concept that whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,873 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being non-silent (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at www.sysbio.se/cenpk. Keywords: Two strains and two different carbon sources Overall design: Two conditions (glucose and galactose) with two biological replicates for S. cerevisiae strains S288c and CEN.PK113-7D

Project description:Wild type strain CEN.PK113-7D was grown in an aerobic batch cultivation with a start concentration of 20 g/L galactose. During exponential growth at a biomass concentration of 3 g dry weight/L LiCl was added to a concentration of 10 mM. Just before addition of LiCl (time 0) and 20, 40, 60 and 140 minutes after addition of LiCl samples were taken for transcription analysis. Lithium inhibits phosphoglucomutase whereby both galactose uptake and growth is strongly affected.

Project description:Genetic and environmental factors influence the phenotype of an organism. Time is rarely considered when studying changes in cellular phenotype. Time-resolved microarray data revealed genome-wide transcriptional changes in cells oscillating with ~2 and ~4 h periods. We mapped the global patterns of transcriptional oscillations into a 3-dimensional map to represent different cellular phenotypes of oscillation period. This map shows the dynamic nature of transcripts through time and concentration space, and that they are ordered and coupled to each other. Although cells differed in oscillation periods, transcripts involved in certain processes were conserved in a deterministic way. This ordered timing of biological process may allow cells to grow energetically efficient. Decreased glucose levels in the media were found to increase the redox cycles of yeast strain CEN.PK113-7D. Glucose may have acted as signaling molecules for timing longer catabolic processes in the cell population. As oscillation period lengthened, the peak to trough ratio of transcripts increased and the percent of cells in the unbudded (G0/G1) phase of the cell cycle increased. Gene transcripts appear to be coordinated with metabolic functions and the cell cycle. High-resolution samples over time were collected for CEN.PK113-7D oscillating with 2 hour period.

Project description:Genetic and environmental factors influence the phenotype of an organism. Time is rarely considered when studying changes in cellular phenotype. Time-resolved microarray data revealed genome-wide transcriptional changes in cells oscillating with ~2 and ~4 h periods. We mapped the global patterns of transcriptional oscillations into a 3-dimensional map to represent different cellular phenotypes of oscillation period. This map shows the dynamic nature of transcripts through time and concentration space, and that they are ordered and coupled to each other. Although cells differed in oscillation periods, transcripts involved in certain processes were conserved in a deterministic way. This ordered timing of biological process may allow cells to grow energetically efficient. Decreased glucose levels in the media were found to increase the redox cycles of yeast strain CEN.PK113-7D. Glucose may have acted as signaling molecules for timing longer catabolic processes in the cell population. As oscillation period lengthened, the peak to trough ratio of transcripts increased and the percent of cells in the unbudded (G0/G1) phase of the cell cycle increased. Gene transcripts appear to be coordinated with metabolic functions and the cell cycle. High-resolution samples over time were collected for CEN.PK113-7D oscillating with 4 hour period.

Project description:Reconstructed mutants of yeast by inverse metabolic engineering were characterized by fermentation physiology and tools from systems biology. Six reconstructed mutants of yeast were grown on aerobic batch with galactose as carbon source

Project description:Resistance to agricultural fungicides in the field has created a need for discovering fungicides with new modes of action. DNA microarrays, because they provide information on expression of many genes simultaneously, could help to identify the modes of action. To begin an expression pattern database for agricultural fungicides, transcriptional patterns of Saccharomyces cerevisiae strain S288C genes were analysed following 2-h treatments with I50 concentrations of ergosterol biosynthesis inhibitors commonly used against plant pathogenic fungi. Eight fungicides, representing three classes of ergosterol biosynthesis inhibitors, were tested. To compare gene expression in response to a fungicide with a completely different mode of action, a putative methionine biosynthesis inhibitor (MBI) was also tested. Expression patterns of ergosterol biosynthetic genes supported the roles of Class I and Class II inhibitors in affecting ergosterol biosynthesis, confirmed that the putative MBI did not affect ergosterol biosynthesis, and strongly suggested that in yeast, the Class III inhibitor did not affect ergosterol biosynthesis. The MBI affected transcription of three genes involved in methionine metabolism, whereas there were essentially no effects of ergosterol synthesis inhibitors on methionine metabolism genes. There were no consistent patterns in other up- or downregulated genes between fungicides. These results suggest that inspection of gene response patterns within a given pathway may serve as a useful first step in identifying possible modes of action of fungicides. agricultural sterol biosynthesis inhibitor fungicides. Keywords = agriculture Keywords = ergosterol Keywords = methionine Keywords = fungicide Keywords = Saccharomyces cerevisiae S288C Keywords = biosynthesis

Project description:The RNA sequencing experiment was performed in four different chemostat conditions as control for a CAGE sequencing experiment performed in the same conditions using the industrial relevant S. Cerevisiae strain CEN.PK113-7D